Predicting Pitcher Strike Outs

import pandas as pd

import numpy as np

PitchingData = pd.read_excel("PitchingData.xls")
PitchingData #data of each MLB pitcher's statistics in 2021

	Last Name	First Name	year	Age	AB	Hits	1B	2B	3B	HR	SO	BattingAvg
0	Wainwright	Adam	2021	40	765	168	111	34	2	21	174	0.220
1	Greinke	Zack	2021	38	652	164	105	29	0	30	120	0.252
2	Santana	Ervin	2021	39	251	65	38	16	2	9	52	0.259
3	Petit	Yusmeiro	2021	37	296	69	47	8	2	12	37	0.233
4	Jansen	Kenley	2021	34	236	36	25	7	0	4	86	0.153
...	...	...	...	...	...	...	...	...	...	...	...	...
268	Fleming	Josh	2021	25	410	110	76	22	1	11	65	0.268
269	Peacock	Matt	2021	27	352	107	69	22	3	13	50	0.304
270	Garcia	Luis	2021	25	573	133	81	31	2	19	167	0.232
271	Weathers	Ryan	2021	22	362	101	62	19	0	20	72	0.279
272	Gray	Josiah	2021	24	265	63	32	10	2	19	76	0.238

273 rows × 12 columns

features = ["AB", "Hits", "1B", "2B", "3B", "HR","BattingAvg"]
target = "SO" #sets up our goal to predict strike outs

X = PitchingData[features]
Y = PitchingData[target]

from sklearn.model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)

from sklearn.neighbors import KNeighborsRegressor

knn = KNeighborsRegressor(n_neighbors=3)
knn

KNeighborsRegressor(n_neighbors=3)

knn.fit(X_train, Y_train)

KNeighborsRegressor(n_neighbors=3)

knn.score(X_train, Y_train), knn.score(X_test, Y_test)

(0.9161565642603654, 0.8398375528861681)

Corbin Burnes

from IPython.display import Image #First Image Import. We will cut from here on out
Image("CorbinBurnesMound.jpeg")

#Corbin Burnes Season Batting Stats

CBurnes = {"AB": 612,
           "H": 123,
           "1B": 96,
           "2B": 17,
           "3B": 3,
           "HR": 7,
           "BattingAvg": 0.201}

CBurnesStats = [CBurnes["AB"], CBurnes["H"], CBurnes["1B"], CBurnes["2B"], CBurnes["3B"], CBurnes["HR"], CBurnes["BattingAvg"]]
CBurnesMatchup = [CBurnesStats]
CBurnesMatchup

[[612, 123, 96, 17, 3, 7, 0.201]]

Knn Prediction

Burnes = knn.predict(CBurnesMatchup)

Burnes 
#Knn prediction for how many strikeouts Burnes will throw

array([187.66666667])

Linear Prediction

from sklearn.linear_model import LinearRegression

lr = LinearRegression()
lr

LinearRegression()

lr.fit(X_train, Y_train)
lr.score(X_train, Y_train), lr.score(X_test, Y_test)

(0.8788696089129353, 0.8717657799643881)

Burnes = lr.predict(CBurnesMatchup)

Burnes 
#Linear prediction for how many strike outs Burnes will throw
#More accurate to his actual number 223

array([192.75782211])

Predicting Batters Strike Outs

MLBData1 = pd.read_excel("MLBData1.xls")
MLBData1
#shows batting stats for each mlb player

	Name	Year	Age	AB	PA	Hits	1B	2B	3B	HR	SO	BattingAvg	RBI
0	Aaron Judge	2021	29	550	633	158	95	24	0	39	158	0.287	98
1	Bryce Harper	2021	29	488	599	151	73	42	1	35	134	0.309	84
2	Albert Pujols	2021	41	275	296	65	45	3	0	17	45	0.236	50
3	Miguel Cabrera	2021	38	472	526	121	90	16	0	15	118	0.256	75
4	Rene Rivera	2021	38	69	78	16	11	3	0	2	29	0.232	9
...	...	...	...	...	...	...	...	...	...	...	...	...	...
551	Wander Franco	2021	20	281	308	81	51	18	5	7	37	0.288	39
552	Jarren Duran	2021	25	107	112	23	16	3	2	2	40	0.215	10
553	Ryan Jeffers	2021	24	267	293	53	28	10	1	14	108	0.199	35
554	Owen Miller	2021	25	191	202	39	27	8	0	4	54	0.204	18
555	Andrew Vaughn	2021	23	417	469	98	61	22	0	15	101	0.235	48

556 rows × 13 columns

#Batting Stats of all MLB players with interaction included 

from ipywidgets import widgets, interactive, Layout
w_player = widgets.Dropdown(
    description = 'player:',
    options = ["All"] + sorted(list(set(MLBData1.Name))),  # a sorted list of unique playerIDs plus All
    value = "All",
    style = {"description_width": '50px'},
    layout = Layout(width="15%")
)

def view(player):
    if player == "All":
        df_tmp = MLBData1
    else:
        df_tmp = MLBData1[MLBData1.Name == player]
        display(df_tmp)

    title = "Batting Stats of Players".format(player)
    df_tmp[["AB", "PA", "Hits", "1B", "2B", "3B", "HR", "SO", "BattingAvg", "RBI"]].plot(kind="bar", title=title, grid=True, figsize=(15,7))

i = interactive(view, player=w_player)
display(i)

features = ["AB", "PA", "Hits", "1B", "2B", "3B", "HR", "RBI","BattingAvg"]
target = "SO"

X = MLBData1[features]
Y = MLBData1[target]

from sklearn.model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.5, random_state = 0)

from sklearn.neighbors import KNeighborsRegressor

knn = KNeighborsRegressor(n_neighbors=3)
knn

KNeighborsRegressor(n_neighbors=3)

knn.fit(X_train, Y_train)
knn.score(X_train, Y_train), knn.score(X_test, Y_test)

(0.8725679962838475, 0.8152481733672445)

# Next, we each chose a hitter to analyze

Jose Altuve

Image("JoseAltuveBatting.jpeg")

#Jose Altuve's Season Batting Stats

JAltuve = {"AB": 601,       
           "PA": 678,
           "H": 167,
           "1B": 103,
           "2B": 32,
           "3B": 1,
           "HR": 31,
           "RBI": 83,
           "BattingAvg": 0.278}

JAltuveStats = [JAltuve["AB"], JAltuve["PA"], JAltuve["H"], JAltuve["1B"], JAltuve["2B"], JAltuve["3B"], JAltuve["HR"], JAltuve["RBI"], JAltuve["BattingAvg"]]
JAltuveMatchup = [JAltuveStats]
JAltuveMatchup

[[601, 678, 167, 103, 32, 1, 31, 83, 0.278]]

Knn Prediction

AltuveSO = knn.predict(JAltuveMatchup)
AltuveSO

array([145.66666667])

AltuveSO 
#Knn prediction for how many times Altuve strikes out

array([145.66666667])

Linear Prediction

from sklearn.linear_model import LinearRegression
lr = LinearRegression()

lr.fit(X_train, Y_train)
lr.score(X_train, Y_train), lr.score(X_test, Y_test)

(0.8319171593242987, 0.8576345797150282)

lr.predict(JAltuveMatchup)
#Linear prediction for strike outs

array([135.47779906])

Bryce Harper

Image("BryceHarperBatting.jpeg")

#Bryce Harpers's Season Batting Stats

BHarper = {"AB": 488,
           "PA": 599,
           "H": 151,
           "1B": 73,
           "2B": 42,
           "3B": 1,
           "HR": 35,
           "RBI": 84,
           "BattingAvg": 0.309}

BHarperStats = [BHarper["AB"], BHarper["PA"], BHarper["H"], BHarper["1B"], BHarper["2B"], BHarper["3B"], BHarper["HR"], BHarper["RBI"], BHarper["BattingAvg"]]
BHarperMatchup = [BHarperStats]
BHarperMatchup

[[488, 599, 151, 73, 42, 1, 35, 84, 0.309]]

Knn Prediction

knn.predict(BHarperMatchup)
#knn prediction for strikeouts

array([116.33333333])

Linear Prediction

lr.predict(BHarperMatchup)
#linear strike out prediction

array([115.66207859])

Nolan Arenado

Image("NolanArenadoBatting.jpeg")

#Nolan Arenado's Season Batting Stats

NArenado = {"AB": 593,
           "PA": 653,
           "H": 151,
           "1B": 80,
           "2B": 34,
           "3B": 3,
           "HR": 34,
           "RBI": 105,
           "BattingAvg": 0.255}

NArenadoStats = [NArenado["AB"], NArenado["PA"], NArenado["H"], NArenado["1B"], NArenado["2B"], NArenado["3B"], NArenado["HR"], NArenado["RBI"], NArenado["BattingAvg"]]
NArenadoMatchup = [NArenadoStats]
NArenadoMatchup

[[593, 653, 151, 80, 34, 3, 34, 105, 0.255]]

Knn Prediction

knn.predict(NArenadoMatchup)
#knn prediction

array([127.33333333])

Linear Prediction

lr.predict(NArenadoMatchup)
#linear prediction

array([148.23801846])

Aaron Judge

Image("AaronJudgeBatting.jpeg")

#Aaron Judge's Season Batting Stats

AJudge = {"AB": 550,
           "PA": 633,
           "H": 158,
           "1B": 95,
           "2B": 24,
           "3B": 0,
           "HR": 39,
           "RBI": 98,
           "BattingAvg": 0.287}

AJudgeStats = [AJudge["AB"], AJudge["PA"], AJudge["H"], AJudge["1B"], AJudge["2B"], AJudge["3B"], AJudge["HR"], AJudge["RBI"], AJudge["BattingAvg"]]
AJudgeMatchup = [AJudgeStats]
AJudgeMatchup

[[550, 633, 158, 95, 24, 0, 39, 98, 0.287]]

Knn Prediction

knn.predict(AJudgeMatchup)
#knn prediction for Judge

array([120.33333333])

Linear Prediction

lr.predict(AJudgeMatchup)
#linear prediction for Judge

array([134.76256019])

Predicting Pitches/Hits

#Fastball stats for MLB Players of our choice

fastball = pd.read_excel("FastBall.xlsx")
fastball

	PlayerID	#Fastballs	%Fastballs	At Bats	FastballH	AvgFastballs	Fastball1B	Fastball2B	Fastball3B	FastballHR	FastballSO
0	J.Altuve	1346	54.1	600	94	0.156667	56	17	1	20	34
1	B.Harper	1277	53.3	488	81	0.165984	36	22	1	22	62
2	N.Arenado	1385	56.8	591	74	0.125212	34	22	0	18	43
3	A.Judge	1345	51.1	545	94	0.172477	56	13	0	25	60
4	C.Burnes	1632	62.9	678	96	0.141593	73	14	3	6	134

#Bar Chart of batting stats vs. fastball pitch with interaction included


from ipywidgets import widgets, interactive, Layout
w_player = widgets.Dropdown(
    description = 'player:',
    options = ["All"] + sorted(list(set(fastball.PlayerID))),  # a sorted list of unique playerIDs plus All
    value = "All",
    style = {"description_width": '50px'},
    layout = Layout(width="15%")
)

def view(player):
    if player == "All":
        df_tmp = fastball
    else:
        df_tmp = fastball[fastball.PlayerID == player]
        display(df_tmp)

    title = "Batting Stats of Players vs Fastballs".format(player)
    df_tmp[["Fastball1B", "Fastball2B", "Fastball3B", "FastballHR", "FastballSO"]].plot(kind="bar", title=title, grid=True, figsize=(15,7))

i = interactive(view, player=w_player)
display(i)

#Breakingball stats for MLB Players of our choice

breakingball = pd.read_excel("BreakingBall.xlsx")
breakingball

	PlayerID	#Breaking	%Breaking	At Bats	BreakingH	AvgBreaking	Breaking 1B	Breaking2B	Breaking3B	BreakingHR	BreakingSO
0	J.Altuve	799	32.1	600	53	0.088333	34	12	0	7	41
1	B.Harper	734	30.6	488	38	0.077869	20	9	0	9	56
2	N.Arenado	856	35.1	591	53	0.089679	33	8	2	10	46
3	A.Judge	894	34.0	545	45	0.082569	29	9	0	7	60
4	C.Burnes	712	27.5	678	19	0.028024	16	2	0	1	84

#Bar Chart of batting stats vs. breakingball pitch with interaction included


from ipywidgets import widgets, interactive, Layout
w_player = widgets.Dropdown(
    description = 'player:',
    options = ["All"] + sorted(list(set(breakingball.PlayerID))),  # a sorted list of unique playerIDs plus All
    value = "All",
    style = {"description_width": '50px'},
    layout = Layout(width="15%")
)

def view(player):
    if player == "All":
        df_tmp = breakingball
    else:
        df_tmp = breakingball[breakingball.PlayerID == player]
        display(df_tmp)

    title = "Batting Stats of Players vs Fastballs".format(player)
    df_tmp[["Breaking 1B", "Breaking2B", "Breaking3B", "BreakingHR", "BreakingSO"]].plot(kind="bar", title=title, grid=True, figsize=(15,7))

i = interactive(view, player=w_player)
display(i)

#Offspeed stats for MLB Players of our choice

offspeed = pd.read_excel("OffSpeed.xlsx")
offspeed

	PlayerID	#OffSpeed	%OffSpeed	At Bats	OffSpeedH	AvgOffspeed	Offspeed1B	OffSpeed2B	OffSpeed3B	OffSpeedHR	OffSpeedSO
0	J.Altuve	341	13.7	600	19	0.031667	12	3	0	4	16
1	B.Harper	385	16.1	488	32	0.065574	17	11	0	4	16
2	N.Arenado	199	8.2	591	24	0.040609	13	4	1	6	6
3	A.Judge	393	14.9	545	17	0.031193	10	2	0	5	37
4	C.Burnes	250	9.6	678	8	0.011799	7	1	0	0	16

#Bar Chart of batting stats vs. offspeed pitch with interaction included


from ipywidgets import widgets, interactive, Layout
w_player = widgets.Dropdown(
    description = 'player:',
    options = ["All"] + sorted(list(set(offspeed.PlayerID))),  # a sorted list of unique playerIDs plus All
    value = "All",
    style = {"description_width": '50px'},
    layout = Layout(width="15%")
)

def view(player):
    if player == "All":
        df_tmp = offspeed
    else:
        df_tmp = offspeed[offspeed.PlayerID == player]
        display(df_tmp)

    title = "Batting Stats of Players vs Fastballs".format(player)
    df_tmp[["Offspeed1B", "OffSpeed2B", "OffSpeed3B", "OffSpeedHR", "OffSpeedSO"]].plot(kind="bar", title=title, grid=True, figsize=(15,7))

i = interactive(view, player=w_player)
display(i)

Putting it all Together

#Our MLB players of choice and their batting stats for each pitching category

PitchTypeData = pd.read_excel("PitchTypeData.xlsx")
PitchTypeData

	PlayerID	PitchType	#Pitches	PercentPitch	At Bats	Hits	Average	1B	2B	3B	HR	SO
0	J.Altuve	Fastball	1346	54.1	600	94	0.156667	56	17	1	20	34
1	B.Harper	Fastball	1277	53.3	488	81	0.165984	36	22	1	22	62
2	N.Arenado	Fastball	1385	56.8	591	74	0.125212	34	22	0	18	43
3	A.Judge	Fastball	1345	51.1	545	94	0.172477	56	13	0	25	60
4	C.Burnes	Fastball	1632	62.9	678	96	0.141593	73	14	3	6	134
5	J.Altuve	Offspeed	341	13.7	600	19	0.031667	12	3	0	4	16
6	B.Harper	Offspeed	385	16.1	488	32	0.065574	17	11	0	4	16
7	N.Arenado	Offspeed	199	8.2	591	24	0.040609	13	4	1	6	6
8	A.Judge	Offspeed	393	14.9	545	17	0.031193	10	2	0	5	37
9	C.Burnes	Offspeed	250	9.6	678	8	0.011799	7	1	0	0	16
10	J.Altuve	Breaking Ball	799	32.1	600	53	0.088333	34	12	0	7	41
11	B.Harper	Breaking Ball	734	30.6	488	38	0.077869	20	9	0	9	56
12	N.Arenado	Breaking Ball	856	35.1	591	53	0.089679	33	8	2	10	46
13	A.Judge	Breaking Ball	894	34.0	545	45	0.082569	29	9	0	7	60
14	C.Burnes	Breaking Ball	712	27.5	678	19	0.028024	16	2	0	1	84

#Bar Chart of batting stats vs. pitch types with interaction included, data combined into one chart.

from ipywidgets import widgets, interactive, Layout
w_player = widgets.Dropdown(
    description = 'player:',
    options = ["All"] + sorted(list(set(PitchTypeData.PlayerID))),  # a sorted list of unique playerIDs plus All
    value = "All",
    style = {"description_width": '50px'},
    layout = Layout(width="15%")
)

def view(player):
    if player == "All":
        df_tmp = PitchTypeData
    else:
        df_tmp = PitchTypeData[PitchTypeData.PlayerID == player]
        display(df_tmp)

    title = "Batting Stats of Players vs Pitch Types".format(player)
    df_tmp[["1B", "2B", "3B", "HR", "SO"]].plot(kind="bar", title=title, grid=True, figsize=(15,7))

i = interactive(view, player=w_player)
display(i)

Corbin Burnes

#Basic pie chart format downloaded from matplotlib.org
#Percent of each Corbin Burnes pitch categories thrown

import matplotlib.pyplot as CBurnes

labels = 'Fastball', 'Breaking Ball', 'Offspeed'
sizes = [62.9, 27.5, 9.6]
explode = (0.1, 0, 0)

fig1, ax1 = CBurnes.subplots()
ax1.pie(sizes, explode = explode, labels = labels, autopct = '%1.1f%%',shadow = True, startangle = 90)
ax1.axis('equal')

CBurnes.show()

Pitch = {"1": "Fastball", "2": "BreakingBall", "3": "Offspeed"}

Next 4 cells are predicting what pitch Burnes will throw. All are the same formula, but are shown that just because the first pitch is a fastball (for example) the next pitch can change

#Random selection of 1,2, or 3 / probabilities of each pitch are listed and the random choice selects with these in mind
Pitch1 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch1]

'Offspeed'

Pitch2 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch2]

'Fastball'

Pitch3 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch3]

'Fastball'

Pitch4 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch4]

'BreakingBall'

Jose Altuve

#Basic pie chart format downloaded from matplotlib.org
#Percent of each pitching category thrown to Jose Altuve

import matplotlib.pyplot as JAltuvePie

labels = 'Fastball', 'Breaking Ball', 'Offspeed'
sizes = [54.1, 32.1, 13.7]
explode = (0.1, 0, 0)

fig1, ax1 = JAltuvePie.subplots()
ax1.pie(sizes, explode = explode, labels = labels, autopct = '%1.1f%%',shadow = True, startangle = 90)
ax1.axis('equal')

JAltuvePie.show()

Next 3 cells are to demonstrate what kind of hit Altuve would get depending on what pitch was thrown from the above cells that predict Burnes's throw

If Fastball Thrown

#For each hitter, we took their hitting probabilities against each pitch and inserted them into another random choice function.
AltuveF = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.44, .13, .01, .16, .26])

If Breakingball Thrown

AltuveB = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.36, .13, 0, .07, .44])

If Offspeed Thrown

AltuveO = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.34, .09, .0, .11, .46])

For Final Pitch (3rd Pitch) the final variable will change to Strike Out instead of Strike

For each batter we conducted a matchup consisting of 3 pitches. Probabilities of each hit for each player are listed to the right and remaining probability is for a strike. If a player reaches a third pitch, probability for strike is renamed strikeout.

Matchup 1

First up to bat against the Cy Young winner Corbin Burnes is Jose Altuve from the Houston Astros! Here comes the first pitch from Burnes...

Pitch5 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch5]

'BreakingBall'

AltuveB

'strike'

Pitch6 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch6]

'Fastball'

AltuveF

'single'

And it's a single from Jose Altuve!!

Image("JoseAltuveCelebrating.jpeg")

And Next Up: Bryce Harper!

Bryce Harper

#Basic pie chart format downloaded from matplotlib.org
#Percent of each pitching category thrown to Bryce Harper

import matplotlib.pyplot as BHarperPie

labels = 'Fastball', 'Breaking Ball', 'Offspeed'
sizes = [53.3, 30.6, 16.1]
explode = (0.1, 0, 0)

fig1, ax1 = BHarperPie.subplots()
ax1.pie(sizes, explode = explode, labels = labels, autopct = '%1.1f%%',shadow = True, startangle = 90)
ax1.axis('equal')

BHarperPie.show()

We then went through and did the same thing for Harper and other batters following (as seen below)

If Fastball Thrown

HarperF = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.25, .15, .01, .15, .44])

If Breakingball Thrown

HarperB = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.21, .10, 0, .10, .59])

If Offspeed Thrown

HarperO = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.35, .23, 0, .08, .34])

For Final Pitch (3rd Pitch) the final variable will change to Strike Out instead of Strike

Matchup 2

Next up to the plate is Bryce Harper from the Philadelphia Phillies. Here comes the first pitch from Burnes!

Pitch7 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch7]

'BreakingBall'

HarperB

'strike'

Pitch8 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch8]

'Fastball'

HarperF

'single'

And it's a Single from Bryce Harper!

Image("BryceHarperCelebrating.jpeg")

And Next Up: Nolan Arenado!

Nolan Arenado

#Basic pie chart format downloaded from matplotlib.org
#Percent of each pitching category thrown to Nolan Arenado


import matplotlib.pyplot as NArenadoPie

labels = 'Fastball', 'Breaking Ball', 'Offspeed'
sizes = [56.8, 35.1, 8.2]
explode = (0.1, 0, 0)

fig1, ax1 = NArenadoPie.subplots()
ax1.pie(sizes, explode = explode, labels = labels, autopct = '%1.1f%%',shadow = True, startangle = 90)
ax1.axis('equal')

NArenadoPie.show()

If Fastball Thrown

ArenadoF = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.29, .19, 0, .15, .37])

If Breakingball Thrown

ArenadoB = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.33, .08, .02, .10, .47])

If Offspeed Thrown

ArenadoO = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.43, .13, .04, .2, .2])

For Final Pitch (3rd Pitch) the final variable will change to Strike Out instead of Strike

Matchup 3

Our third matchup of the night is Nolan Arenado from the St. Louis Cardinals. Let's see what he can do against the Cy Young winner, here comes the first pitch.

Pitch10 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch10]

'Fastball'

ArenadoF

'homerun'

Image("NolanArenadoCelebrating.jpeg")

And we can't believe it folks! A first pitch HOMERUN from Nolan Arenado! The crowd is going nuts!

And Next Up: Aaron Judge

Aaron Judge

#Basic pie chart format downloaded from matplotlib.org
#Percent of each pitching category thrown to Aaron Judge


import matplotlib.pyplot as AJudgePie

labels = 'Fastball', 'Breaking Ball', 'Offspeed'
sizes = [51.1, 34, 14.9]
explode = (0.1, 0, 0)

fig1, ax1 = AJudgePie.subplots()
ax1.pie(sizes, explode = explode, labels = labels, autopct = '%1.1f%%',shadow = True, startangle = 90)
ax1.axis('equal')

AJudgePie.show()

If Fastball Thrown

JudgeF = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.37, .08, 0, .16, .39])

If Breakingball Thrown

JudgeB = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.28, .08, 0, .07, .57])

If Offspeed Thrown

JudgeO = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'strike'], p = [.18, .04, 0, .09, .69])

For Final Pitch (3rd Pitch) the final variable will change to Strike Out instead of Strike

Matchup 4

And last up to the plate is Aaron Judge from the New York Yankess! He has been on a terror this year let's see what he can do for our last matchup of the night. Here comes the first pitch!

Pitch11 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch11]

'Offspeed'

JudgeO

'strike'

Pitch12 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch12]

'Offspeed'

JudgeO

'strike'

Pitch13 = np.random.choice(a=["1", "2", "3"], p = [0.63, 0.27, 0.10])
Pitch[Pitch13]

'Fastball'

JudgeF3 = np.random.choice(a=['single', 'double', 'triple', 'homerun', 'Strike Out'], p = [.37, .08, 0, .16, .39])
JudgeF3 #This statement was made for the final pitch, changing strike to strikeout. Judge was the only player to reach this point.

'Strike Out'

Image("AaronJudgeSad.jpeg")

Image("CorbinBurnesCelebrating.jpeg")

And DOWN GOES JUDGE. Just look at Burnes celebrating out there! Well Folks, that's a wrap for the night. Hope you all enjoyed the show!